Special Service and Features
IIT Madras Researchers use AI tools to study Production of Fuel from biomass
Indian Institute of Technology MadrasResearchers are using Artificial Intelligence tools to study the processes involved in conversion of biomass to gaseous fuel. Gaining such understanding through hands-on experiments is time-consuming and expensive. Computer simulations and modelling studies can provide quicker insights that can be used to build the processes and plants for biomass processing.
With increasing environmental concerns associated with petroleum-derived fuels, biomass is a practical solution, not in the conventional sense of directly burning wood, cow dung cakes, and coal, but as a source of energy-dense fuel. Researchers all over the world are finding methods to extract fuel from biomass such as wood, grass, and even waste organic matter.
Such biomass-derived fuel is particularly relevant to India because the current availability of biomass in India is estimated at about 750 million metric tonnes per year and extracting fuel from them can tremendously help the country attain fuel self-sufficiency.
The research was led by Dr. Himanshu Goyal, Assistant Professor, Department of Chemical Engineering, IIT Madras and Dr. Niket S Kaisare, Professor, Department of Chemical Engineering, IIT Madras.
A video byte of Dr Himanshu Goyal explaining this research can be viewed and downloaded from the following link - https://drive.google.com/file/d/1LUPLaH1RV7iypV9EaIY2llvrdWen0O-F/view?usp=sharing
Recent results of their modelling studies were published in the prestigious peer-reviewed Royal Society of Chemistry journal Reaction Chemistryand Engineering . The paper has been co-authored by Dr. Himanshu Goyal, Dr. NiketKaisare and Mr. Krishna Gopal Sharma, Fourth Year B.Tech.Student, Department of Computer Science and Engineering, IIT Madras.
Explaining the importance of such studies, Dr. Himanshu Goyal, Assistant Professor, Department of Chemical Engineering, IIT Madras, said, “Understanding the complex mechanisms involved in the conversion of raw biomass into fuel is important for designing the processes and optimizing reactors for the purpose.”
Further, Dr. Himanshu Goyal said, “There is an urgent need to train the next generation of engineers on high-performance computing and machine learning skills so that they can address some of the biggest challenges before us, such as developing zero-emission technologies to tackle climate change. This work is one such example.”
While models are being developed all over the world to understand the conversion of biomass into fuels and chemicals, most models take a long time to become operational. Artificial Intelligence tools such as Machine Learning  can hasten the modelling processes.
The IIT Madras research team used an ML method called Recurrent Neural Networks  to study the reactions that occur during the conversion of lignocellulosic biomass into energy dense syngas .
Elaborating further, Dr. Niket S Kaisare, Professor, Department of Chemical Engineering, IIT Madras, “The novelty of our ML approach is that it is able to predict the composition of the biofuel produced as a function of the time the biomass spends in the reactor. We used a statistical reactor for accurate data generation, which allows the model to be applied over a wide range of operating conditions.”
Dr. Himanshu Goyal’s Research Group uses AI tools not only for biomass-biofuel conversion studies but also for socially relevant and environmentally beneficial processes such as carbon capture  and the of electrification of the chemical industry.
The team believes that the rapid advancements in computational methods must be integrated with core engineering for faster development and deployment of deep tech solutions. Such developments cannot be constrained by specialities and departments.
While the lead researchers, Dr Goyal and Dr Kaisare are from the Department of Chemical Engineering at IITM, the student researcher, Krishna Gopal Sharma, is a computer science undergrad and Young Research Fellow of the institute.
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